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Optimal credibility estimation of random parameters in hierarchical random effect linear model

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Abstract

In the hierarchical random effect linear model, the Bayes estimator of random parameter are not only dependent on specific prior distribution but also it is difficult to calculate in most cases. This paper derives the distributed-free optimal linear estimator of random parameters in the model by means of the credibility theory method. The estimators the authors derive can be applied in more extensive practical scenarios since they are only dependent on the first two moments of prior parameter rather than on specific prior distribution. Finally, the results are compared with some classical models and a numerical example is given to show the effectiveness of the estimators.

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Authors and Affiliations

  1. School of Mathematics and Information Science, Jiangxi Normal University, Nanchang, 330022, China

    Limin Wen, Jing Fang & Guoping Mei

  2. School of Information Management, Jiangxi University of Finance and Economics, Nanchang, 330013, China

    Limin Wen & Guoping Mei

  3. Department of Statistics and Actuarial Science, East China Normal University, Shanghai, 200062, China

    Xianyi Wu

Authors
  1. Limin Wen
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  2. Jing Fang
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  3. Guoping Mei
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  4. Xianyi Wu
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Corresponding author

Correspondence to Limin Wen.

Additional information

This research was supported by the National Science Foundation of China under Grant Nos. 71361015, 71340010, 71371074, the Jiangxi Provincial Natural Science Foundation under Grant No. 20142BAB201013, China Postdoctoral Science Foundation under Grant No. 2013M540534, China Postdoctoral Fund special Project under Grant No. 2014T70615 and Jiangxi Postdoctoral Science Foundation under Grant No. 2013KY53.

This paper was recommended for publication by Editor LIU Yungang.

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Wen, L., Fang, J., Mei, G. et al. Optimal credibility estimation of random parameters in hierarchical random effect linear model. J Syst Sci Complex 28, 1058–1069 (2015). https://doi.org/10.1007/s11424-015-3202-5

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  • DOI: https://doi.org/10.1007/s11424-015-3202-5

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